Search Results (1,794)

Background/Objectives: Disease-related malnutrition is highly prevalent in oncology and is associated with poor clinical outcomes. Early detection through nutritional screening is essential; however, the optimal screening tool for ambulatory cancer patients remains uncertain. This study aimed to evaluate the agreement and diagnostic performance of the malnutrition screening tool (MST) and NUTRISCORE compared with the Global Leadership Initiative on Malnutrition (GLIM) criteria in a large nationwide cohort of ambulatory cancer patients. Methods: In this multicenter, observational, cross-sectional nationwide study, adult patients attending oncology day hospitals for intravenous antineoplastic treatment between April and November 2021 were included. Nutritional risk was assessed using MST (cut-off ≥ 2) and NUTRISCORE (cut-off ≥ 5). Malnutrition was diagnosed according to GLIM criteria. Agreement between tools was assessed with Cohen’s kappa, and diagnostic performance was evaluated by sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Analyses were stratified by tumor nutritional risk and cancer stage. Results: Among 4440 patients from 86 hospitals, 50.7% met the GLIM criteria for malnutrition; 72.5% had moderate and 27.5% severe malnutrition. MST identified 37.5% of patients as being at nutritional risk, compared with 17.3% identified by NUTRISCORE. Agreement between MST and NUTRISCORE was moderate overall (κ = 0.48; 95% CI, 0.45–0.51), but varied markedly according to tumor nutritional risk, ranging from high agreement in high-risk tumors (κ = 0.82) to low agreement in low-risk tumors (κ = 0.28). Relative to GLIM, MST was more sensitive than NUTRISCORE (0.51 vs. 0.27), whereas NUTRISCORE was more specific (0.92 vs. 0.76) and had a higher positive predictive value (0.77 vs. 0.68). Negative predictive value was low for both tools. Conclusions: GLIM-defined malnutrition was highly prevalent in this large cohort of ambulatory patients with cancer. MST provided greater case detection, whereas NUTRISCORE showed a more conservative profile with higher specificity but substantially lower sensitivity. These findings suggest that the choice of screening tool should consider clinical context- and tumor-related nutritional risk, and that neither instrument alone reliably excludes malnutrition in outpatient oncology settings. Full article

This paper studies capacity planning for a wind–hydrogen integrated energy system under scenario-based uncertainty in wind generation, hydrogen demand, and electricity prices. The model is formulated as a two-stage stochastic program in which first-stage investment decisions are selected before uncertainty is realized and second-stage hourly operation is optimized for each representative scenario. The main methodological difficulty is that part of the first-stage hydrogen-storage investment cost may be available only through a non-analytical evaluator, such as supplier quotation logic, simulation software, or a data-driven estimator, while the operational recourse model remains linear. To address this setting, a hybrid heuristic–Benders framework, denoted as GSOA-Benders, is developed by coupling the General-Soldiers Optimization Algorithm for derivative-free first-stage search with Benders cuts generated from linear programming subproblems. The framework is not presented as a replacement for commercial solvers on explicit convex or mixed-integer models; rather, it is intended for cases where exact algebraic reformulation of the first-stage cost is unreliable or unavailable. In the black-box case study with 500 scenarios, the method converges in 35.86 s and obtains an investment plan expressed as $\mathbf{x}=[1,0.53,23.23,0]$, corresponding to wind-farm construction, a 0.53 MW electrolyzer, a 23.23 MWh hydrogen tank, and no fuel-cell investment. Additional discussion is provided on stability-gap interpretation, benchmark limitations, component lifetime assumptions, hydrogen losses, and environmental extensions. Full article

Hybrid robotic manufacturing systems integrating additive and subtractive processes enable fabrication of complex, high-value components but are typically executed sequentially, resulting in long cycle times. Concurrent execution of Directed Energy Deposition (DED) and milling promises productivity gains but introduces coupled thermal, mechanical and spatial interactions that challenge conventional process planning. This work addresses the methodological problem of planning milling operations in the presence of an ongoing DED process. The concurrent planning task is formulated as a mixed-integer, nonlinear, multi-objective optimisation problem capturing sequencing and orientation decisions, cutting parameters and enabling temporal coupling to the deposition trajectory. A hierarchical, surrogate-assisted optimisation framework is proposed, combining unified decision-variable encoding, deterministic decoding and staged feasibility enforcement to ensure robotic executability. Disturbance mechanisms such as thermal interaction, particulate interference and pose-dependent dynamic compatibility are incorporated as modular objective abstractions, enabling systematic trade-offs between machining productivity and preservation of deposition process integrity. The proposed framework is demonstrated on a representative case study, enabling analysis of the interaction between spatial sequencing, temporal feasibility and disturbance-aware optimisation. The case study provides a controlled instantiation and illustrates its application to concurrent additive–subtractive planning under explicitly modelled temporal and disturbance constraints. Full article

Background: Weight loss and skeletal muscle wasting are frequent in cancer and may influence treatment tolerance and outcomes. Computed tomography (CT) based body composition analysis at the third lumbar vertebra (L3) is an accurate method to quantify skeletal muscle in routine oncology care. Methods: We performed a multicenter retrospective cohort study including 202 adults with locally advanced (stage IB–III) gastric cancer treated in four Portuguese hospitals (January 2020–December 2022). Skeletal muscle area (SMA) was assessed on baseline CT at the L3 vertebral level, using Data Analysis Facilitation Suite (DAFS) software v3.11.2, and skeletal muscle index (SMI) was subsequently calculated. Patients with low muscle quantity were classified as sarcopenic (below sex-specific SMI mean). We evaluated associations with relapse-free survival (RFS), overall survival (OS), FLOT chemotherapy dose-limiting toxicities (DLTs), and postoperative complications after gastrectomy. Results: Mean age was 69 years, 65% had ECOG PS 0, 53% received FLOT chemotherapy protocol. Mean SMI was 49.6 cm²/m² in males and 40.9 cm²/m² in females and correlated positively, though moderately, with BMI (p < 0.01; r = 0.424). Sarcopenia was not significantly associated with RFS (p = 0.186) or OS (p = 0.168) at 30-month follow-up. Although numerical differences were observed (64% vs. 56% of patients did not relapse and 74% vs. 63% were alive, for non-sarcopenic vs. sarcopenic patients). Sarcopenia was associated with a higher risk of DLTs (p = 0.021; OR 2.56, 95% CI 1.15–5.73) and postoperative complications (p = 0.024; OR 2.16, 95% CI 1.11–4.21). Conclusions: Sarcopenia significantly increases the risk of chemotherapy toxicity and postoperative complications in locally advanced gastric cancer. However, its effect on OS and RFS was not statistically significant at 30-month follow-up. Standardization of CT-based sarcopenia cut-offs remains a major barrier to clinical implementation. Full article

Hybrid gas separation technologies combining different physicochemical mechanisms represent a promising approach for the efficient treatment of complex natural gas mixtures. In this work, a hybrid process integrating gas hydrate crystallization and membrane gas separation was investigated for the upgrading of multicomponent natural gas-containing hydrocarbons (C₁–C₄), acid gases (CO₂ and H₂S), and inert components. Polysulfone hollow-fiber membranes were fabricated, and their gas transport properties were experimentally determined using an eight-component quasi-real natural gas mixture under elevated pressure conditions. The obtained mixed-gas permeance values were used as input parameters for the development of a detailed mathematical model of a hollow-fiber membrane module implemented in the Aspen Custom Modeler. The model was applied to simulate membrane separation of both gas- and hydrate-derived streams produced by the gas hydrate crystallizer. Simulation results were analyzed in terms of hydrocarbon composition, acid gas removal efficiency, and hydrocarbon recovery as a function of the stage-cut. The modeling predictions were validated experimentally using a laboratory membrane module integrated with the gas hydrate crystallization unit. Good agreement between the experimental data and simulation results was observed for all major components. The deviation between modeled and experimental concentrations remained small, while the discrepancy in hydrocarbon recovery was higher and reached approximately 10–20%, which is attributed to the cumulative uncertainty of flow rate and composition measurements. These results confirm the adequacy of the developed model. The hybrid process demonstrates strong complementarity between the thermodynamic selectivity of hydrate formation and the transport selectivity of membrane separation, enabling efficient removal of acid gases while maintaining acceptable hydrocarbon recovery. The results indicate that the proposed gas hydrate–membrane hybrid process is a promising strategy for advanced natural gas purification and upgrading. Full article

Background: Reliable biomarkers that capture tumor–host interactions and predict treatment resistance in extensive-stage small cell lung cancer (SCLC) remain limited. We evaluated the prognostic and predictive value of the pretreatment lactate dehydrogenase-to-albumin ratio (LAR), an integrative biomarker reflecting metabolic activity, systemic inflammation, and host nutritional status. Methods: This multicenter, retrospective cohort study included patients with extensive-stage SCLC treated at five tertiary centers between 2016 and 2024. Pretreatment LAR was calculated using baseline serum lactate dehydrogenase and albumin levels and dichotomized using a Youden index-derived cut-off at the 12-month overall survival (OS) horizon. Time-dependent receiver operating characteristic (ROC) analyses using inverse probability weighting were performed to assess discriminative performance. Survival outcomes were evaluated using Kaplan–Meier estimates and Cox proportional hazards models. Associations with platinum resistance and lack of objective treatment benefit (defined as progressive disease as best response) were examined using logistic regression models. Results: A total of 223 patients were included. Elevated LAR was associated with inferior OS (median, 15.8 vs. 25.2 months; log-rank p < 0.001) and progression-free survival (7.9 vs. 11.5 months; p < 0.001). In multivariable analysis, LAR remained independently associated with OS (HR, 1.43; 95% CI, 1.04–1.95; p = 0.028). LAR demonstrated modest but consistently superior discriminative performance compared with other inflammatory indices for both 12-month OS (area under the curve [AUC], 0.692) and 6-month progression-free survival (PFS) (AUC, 0.646), with statistically significant differences in DeLong comparisons. Higher LAR was independently associated with increased odds of platinum resistance (adjusted odds ratio [aOR], 2.31; 95% CI, 1.41–3.81; p = 0.001) and lack of objective treatment benefit (adjusted OR, 2.04; 95% CI, 1.33–3.14; p = 0.001). Conclusions: Pretreatment LAR is a clinically accessible and biologically integrative biomarker associated with survival and treatment resistance in extensive-stage SCLC. By capturing tumor–host interactions, LAR may support risk stratification and identify patients at increased risk of early treatment failure. Prospective validation is warranted to define its role in biomarker-driven clinical decision-making. Full article

Meaningful partitioning of 2D binary shapes remains a challenging problem in shape analysis because many existing methods rely mainly on local geometric rules or skeleton simplification, which often struggle to separate the main body of a shape from its protruding parts in a perceptually meaningful way. This limitation becomes more evident in shapes with thin limbs, branching structures, or irregular extensions, where preserving topology while achieving human-consistent decomposition is difficult. We present a fully automatic framework for the hierarchical partitioning of 2D binary shapes into semantically meaningful core bodies and protruding limbs (tails). The pipeline begins by generating candidate structural lines through multi-directional centroid tracking along horizontal, vertical, and diagonal (±45°) bands. Three direction-specific Sugeno fuzzy controllers first evaluate these lines based on normalized length, angular alignment, and minimum distance to the boundary. A second pair of fuzzy systems then classifies segments as either tails or core parts using thickness statistics derived from the distance transform. For ambiguous merged tail groups, iterative midpoint splitting is applied until stable labeling is achieved. High-curvature boundary corners are then detected via signed turning-angle analysis, and candidate cutting rays are assessed through exact region splitting, tail area measurement, and label purity analysis. An adaptive third-stage fuzzy controller ranks these candidates according to cut length, purity, and area. The highest-scoring non-overlapping cuts are executed iteratively, progressively peeling peripheral parts while preserving the overall topology and symmetry of the shape. The proposed framework is evaluated on a targeted subset of 32 categories from the 2D Shape Structure Dataset Results on this evaluated subset indicate that the method produces coherent and topologically consistent partitions, with competitive agreement with the available human-annotated references. This training-free framework provides an interpretable tool for 2D shape analysis, with potential applications in object recognition, computer animation, and symmetry studies. Full article

Background: Emergency department evaluation of suspected urolithiasis increasingly relies on non-contrast CT, yet not all patients require imaging. Existing clinical prediction rules help stratify stone probability, but by converting continuous measurements into fixed binary indicators, they offer little insight into why a particular patient is at risk or how much uncertainty remains after each testing stage—questions that bear directly on individualized diagnostic decisions. Methods: We retrospectively analyzed 1000 ED patients with suspected urolithiasis who underwent non-contrast CT (stone prevalence 85.0%). A gradient boosting classifier was trained on 17 continuous clinical and laboratory features and compared against binary-thresholded counterparts and an established scoring system; the 17-feature model achieved AUC 0.771 (95% CI 0.726–0.813) versus 0.723 (95% CI 0.675–0.771) for the reference score on this cohort (DeLong p = 0.001). Individual predictions were explained using an interventional Shapley value approach, and a Shannon entropy-based framework was applied to quantify the marginal diagnostic contribution of each sequential testing stage. Results: Held-out permutation importance identified red blood cell count on microscopy, age, pain duration, and prior stone history as the most influential predictors. Several features showed non-linear contributions that diverged from conventional binary thresholds: creatinine effect crossed zero near 0.90 mg/dL and pain duration peaked between 2 and 5 h. C-reactive protein, absent from existing scoring systems, emerged as a meaningful negative predictor. Sequential entropy analysis showed that dipstick urinalysis provided the largest marginal information gain among non-history stages (6.1% of prior entropy), while physical examination contributed 2.3%. A prevalence sensitivity analysis projected that the framework’s threshold behavior would differ substantially in lower-prevalence populations, underscoring that the cohort-specific cut-points are not portable decision rules. We therefore position the framework as a reasoning aid that complements clinical judgment and imaging, not as a stand-alone triage tool. Conclusions: Explainable machine learning can address questions that aggregate discrimination metrics cannot: which features drive risk for a given patient, how those effects behave across the continuous measurement range, and how much diagnostic uncertainty each testing stage resolves. The Shapley-based explanations and entropy framework developed here offer a structured approach to individualized diagnostic reasoning in the ED evaluation of suspected urolithiasis, functioning as an interpretive adjunct to, rather than a replacement for, existing clinical tools and CT imaging. Full article

Microgrid sizing has traditionally been driven by economic, technical, environmental, and social criteria, while safety has often been treated implicitly or addressed at later stages of design and operation. In this context, safety refers to the prevention of unacceptable harm to people, assets, and the environment through appropriate design margins, protection coordination, operational limits, and risk-aware system configuration. However, the increasing penetration of distributed energy resources, battery energy storage systems, power electronics, and advanced digital control architectures has elevated safety to a critical design dimension that directly influences sizing decisions. Despite its importance, safety remains fragmented across the microgrid literature and lacks unified treatment within sizing-oriented studies. This paper presents a systematic review of microgrid sizing methodologies with a specific focus on safety-related indicators. The review critically examines how distinct safety dimensions—namely energy storage safety, protection and fault tolerance, operational margins and redundancy, grid interaction, cybersecurity, human and environmental safety—are addressed within traditional, artificial-intelligence-based, software-driven, and hybrid sizing approaches. Safety is conceptualized as a cross-cutting design constraint that shapes sizing variables and feasibility boundaries rather than as an independent optimization objective. By synthesizing the existing literature, this work identifies the safety dimensions most strongly coupled with sizing decisions. The paper further analyses how safety-related constraints can be incorporated into sizing frameworks and highlights key research gaps that hinder their systematic integration. The findings aim to provide a structured reference for researchers and practitioners seeking to embed safety considerations into microgrid sizing methodologies. Full article

Crude fidaxomicin contains difficult-to-separate impurities, and conventional dual-step purification usually requires intermediate concentration and transfer, which increases process complexity and may aggravate product loss or degradation. To address this challenge, this study exploits the complementary selectivity of methanol/water (80/20, v/v) and acetonitrile/water (70/30, v/v) binary mobile phases and proposes two purification processes based on step-gradient twin-column recycling chromatography, namely spatial integration and system integration. In the spatial integration strategy, dual-stage separations that are conventionally performed in separate chromatographic systems are sequentially integrated into a single twin-column recycling system in combination with on-line heart-cutting, thereby eliminating intermediate off-line processing steps. In contrast, the system integration strategy merges the two binary mobile phases in defined proportions to construct a single ternary mobile phase composed of methanol/acetonitrile/water (37.5/37.5/25, v/v/v), enabling one-step complete separation. The results demonstrate that the spatial integration strategy, employing binary mobile-phase switching, produces fidaxomicin with a purity of 99.9%, recoveries ranging from 75.27% to 78.77%, and productivities ranging from 307.22 to 328.82 g·L⁻¹·day⁻¹, regardless of the switching sequence. The system integration strategy, based on one-step elution with the ternary mobile phase, achieves the same product purity of 99.9% without mobile-phase switching, with a recovery of 70.41% and a productivity of 246.33 g·L⁻¹·day⁻¹. These results confirm the applicability and flexibility of both integrated strategies for fidaxomicin purification, while indicating that the spatial integration strategy provides better overall preparative performance and the system integration strategy offers a simpler one-step operation. Full article

Background/Objectives: The multiple benefits of physical activity and exercise (PAE) for non-communicable diseases (NCDs) and, thus, for public health underscore the importance of their multidisciplinary implementation in clinical practice. However, there is a lack of validated instruments that comprehensively assess physicians’ knowledge, attitudes, intentions, abilities, and behaviour (KAIAB) regarding PAE promotion in NCD management. Methods: This study aimed to develop and validate a new questionnaire to assess physicians’ KAIAB towards PAE and to evaluate their KAIAB levels. A two-stage design, including an e-Delphi method and a cross-sectional study, was conducted in Greece from January 2022 to May 2022. Results: In the first stage, after achieving consensus and stability within a purposive sample of 16 physician–experts (response rate 100%), the questionnaire was effectively developed and validated (Content Validity Ratio: 0.5–1) using a two-round e-Delphi method. In the second stage, a cross-sectional study was conducted in two physician populations from 12 medical specialities (response rate: 18.2%) and demonstrated that the new questionnaire had sufficient face validity and high reliability (Cronbach’s alpha: 0.805– 0.931). The three original Bloom levels’ cut-off points were also used to classify physicians’ KAIAB levels in the second stage. KAIAB levels were assessed using median and interquartile range (Mdn/IQR) and were found to be low (13/6), moderate (128/79), high (35/9), moderate (21/8), and moderate (33/8), respectively. Conclusions: The new questionnaire is reliable and valid. It is recommended that the questionnaire be applied in larger studies to further verify its validity and applicability. Additionally, it was found that although physicians reported high intentions and moderately positive attitudes toward PAE promotion, their knowledge in these domains and their exercise prescription practices remained limited. This underscores the need to enhance policies and initiatives in medical education and the healthcare system. Full article

Unmanned aerial vehicles (UAVs) operating in safety-critical missions require effective anomaly detection methods to identify propulsion-system faults before they cause catastrophic failures. However, current vibration-based diagnostic models typically rely on datasets representing only discrete, isolated fault states, and do not capture the continuous structural degradation that occurs during real flight operations. To address this gap, this study proposes a severity-ordered vibration data augmentation framework for anomaly detection in rotary-wing UAV propulsion systems. Controlled experiments were conducted under healthy, tape-induced imbalance, scratch, and cut propeller conditions using stepped throttle excitation from 10% to 100% in 10% increments, with 40 s per level. A severity-ordered arrangement strategy based on throttle level and a robust peak-to-peak severity metric generated approximately 7.5 h of augmented vibration data per axis, representing a continuous degradation trajectory. Three-axis continuous wavelet transform (CWT) scalograms of size $48\times 96\times 3$ were used to train an unsupervised anomaly detection framework. Comparative experiments with Isolation Forest, One-Class SVM, and LSTM–AE demonstrated that the proposed Convolutional Neural Network (CNN)–Bidirectional Gated Recurrent Unit (BiGRU)–State-Space Model (SSM)–Autoencoder (AE) architecture achieved the best performance, reaching 0.9959 precision, 0.4428 recall, 0.6131 F1-score, and 0.9284 Area Under the Receiver Operating Characteristic Curve (AUROC). The ablation study further showed that incorporating temporal modeling and state-space dynamics improves detection robustness compared with CNN–AE and CNN–BiGRU–AE baselines. These results show that combining severity-ordered augmentation with deep temporal learning improves progressive propulsion anomaly detection in UAV vibration monitoring. This work introduces a methodology that connects rotor dynamics principles with deep learning, providing a continuous degradation manifold that improves early-stage detection and condition monitoring of UAV propulsion systems. Full article

In high-value precision machining, existing tool wear monitoring models often suffer from two major limitations: poor generalization under varying cutting conditions and heavy reliance on large amounts of labeled data for new operating scenarios. These limitations hinder the practical deployment of intelligent monitoring systems. To address these challenges, this paper proposes a few-shot warm-start framework based on model-agnostic meta-learning. The method consists of two stages. First, meta-training is performed on historical machining data to learn a task-sensitive parameter initialization that enables rapid adaptation. Second, under a new operating condition, the few-shot warm-start mechanism collects a minimal number (1 to 5) of samples through a targeted physical trial-cutting process for online fine-tuning, aligning the model with the current physical environment. Experiments on the PHM2010 dataset fully simulate varying cutting scenarios. The experimental results demonstrate that the proposed framework consistently outperforms traditional transfer learning, deep learning models, and existing meta-learning approaches, offering an effective solution for fast and accurate tool wear prediction under few-shot and varying cutting conditions. Full article

Introduction: Clinical staging based on digital rectal examination is imprecise, leading to pathological upstaging in patients with prostate cancer (PCa). Accurate preoperative assessment remains a challenge despite the use of multiparametric magnetic resonance imaging (mpMRI) and fusion-guided biopsy. This study aims to identify key predictors of upstaging in preoperative patients. Materials and Methods: A retrospective analysis of 924 patients who underwent radical prostatectomy between July 2012 and January 2025 was performed. Variables included prostate-specific antigen, prostate volume, biopsy type, MRI, body mass index and age. Upstaging was defined as ≥pT3 in patients staged clinically as cT1–2. Optimal cut-offs for continuous variables were defined statistically. Multivariable logistic regression was applied to identify independent predictors of upstaging and minor staging upgrading (MSU)—defined as any upward shift in the pathological T stage relative to the clinical T stage. Model performance was evaluated using the area under the Receiver Operating Characteristic (ROC) curve (AUC). Results: Upstaging occurred in 31.9% and MSU in 50.6% of patients. The mean age was 65 years. Cut-off values for PSA density (PSAD) were 0.29 for upstaging and 0.28 for MSU. In the full-cohort model (AUC = 0.628), PSAD (odds ratio (OR) = 2.55), age (OR = 1.04), and hypertension (HT) (OR = 1.47) were associated with upstaging. In PIRADS-based models, PIRADS 5 and PSAD predicted both upstaging (OR = 1.62 and 6.10, respectively; AUC = 0.664) and MSU (OR = 1.75 and 4.67, respectively; AUC = 0.659). MSU was also associated with HT and a lack of fusion biopsy (AUC = 0.622). Conclusions: PSAD and PIRADS 5 lesions are strong determinants of pathological upstaging and MSU in PCa. These factors should be considered in preoperative risk stratification to improve staging accuracy. Despite advances in imaging and biopsy techniques, upstaging remains a common phenomenon, underlining the need for further refinement of diagnostic protocols. Full article

This study evaluated the morphological development of 23 Coffea canephora clones in Espírito Santo to identify materials with superior vigor and quality for commercial and breeding purposes. Seedlings from cuttings were arranged in a completely randomized design with ten replicates and assessed at the commercial dispatch stage. Shoot and root growth, biomass, leaf area (LA), Dickson Quality Index (DQI), structural ratios (shoot/root ratio, SRR; height/diameter ratio, HDR), and anatomical traits were measured. Data were analyzed using analysis of variance with Scott–Knott clustering, Pearson correlation, and Principal Component Analysis (PCA). Significant variability was observed among clones. Clones 88, VR3, 8, and LB33 showed the highest stem diameter (SD), total dry mass (TDM), LA, and DQI, with balanced shoot and root development. Leaf area correlated strongly with SD, number of leaves (NL), biomass, and DQI, confirming its role as a seedling quality indicator. PCA identified two groups: a high-performance group with greater vigor and biomass, and a lower-performance group including clones 7, MR04, and VR4. The convergence of methods confirms the robustness of the results. Overall, clones 88, VR3, 8, and LB33 demonstrate superior agronomic potential at the seedling stage, offering promising options for nurseries, growers, and clonal selection programs. Full article